Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	86,571
2	South Dakota	76,056
3	Iowa	61,233
4	Wisconsin	58,764
5	Nebraska	53,662
6	Utah	49,581
7	Idaho	47,778
8	Illinois	47,298
9	Montana	46,310
10	Tennessee	46,060
11	Mississippi	45,630
12	Alabama	45,041
13	Arkansas	45,033
14	Louisiana	44,675
15	Kansas	43,135
16	Missouri	42,502
17	Wyoming	42,250
18	Rhode Island	42,032
19	Minnesota	42,024
20	Florida	41,778
21	Nevada	40,284
22	Oklahoma	40,032
23	Indiana	39,334
24	Georgia	39,069
25	Arizona	38,644
26	South Carolina	38,436
27	Texas	38,370
28	Alaska	34,049
29	Kentucky	32,504
30	New Jersey	32,347
31	New Mexico	32,219
32	North Carolina	30,371
33	Delaware	30,348
34	Colorado	30,079
35	Michigan	29,682
36	New York	29,482
37	Maryland	28,151
38	Massachusetts	27,996
39	District of Columbia	27,359
40	Ohio	26,729
41	California	26,717
42	Connecticut	26,641
43	Virginia	24,223
44	Puerto Rico	24,172
45	Pennsylvania	21,947
46	West Virginia	19,710
47	Washington	18,506
48	Oregon	13,886
49	Hawaii	11,833
50	New Hampshire	11,254
51	Maine	6,965
52	Vermont	4,974

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Wyoming	1,481
2	North Dakota	1,353
3	Minnesota	1,236
4	Nebraska	1,201
5	South Dakota	1,168
6	Montana	1,115
7	Wisconsin	1,062
8	Iowa	1,018
9	Rhode Island	943
10	Illinois	927
11	Kansas	908
12	Indiana	858
13	Alaska	856
14	Utah	812
15	Colorado	738
16	Tennessee	730
17	Michigan	702
18	Idaho	700
19	New Mexico	697
20	Oklahoma	691
21	Missouri	687
22	Ohio	633
23	Connecticut	592
24	Nevada	525
25	Kentucky	474
26	West Virginia	470
27	Pennsylvania	431
28	Arkansas	412
29	Louisiana	408
30	New Jersey	404
31	Delaware	396
32	Arizona	361
33	Florida	344
34	Alabama	340
35	Texas	340
36	Massachusetts	329
37	Maryland	328
38	Washington	320
39	North Carolina	280
40	Mississippi	275
41	South Carolina	251
42	New Hampshire	243
43	California	241
44	District of Columbia	233
45	Virginia	232
46	Georgia	226
47	New York	209
48	Oregon	201
49	Puerto Rico	189
50	Maine	141
51	Vermont	139
52	Hawaii	32

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,870
2	New York	1,725
3	Massachusetts	1,503
4	Connecticut	1,338
5	Louisiana	1,324
6	Rhode Island	1,206
7	Mississippi	1,203
8	North Dakota	1,016
9	District of Columbia	935
10	Illinois	893
11	Arizona	868
12	Michigan	852
13	Georgia	826
14	Florida	821
15	South Carolina	807
16	Delaware	758
17	Indiana	746
18	Arkansas	743
19	Pennsylvania	736
20	South Dakota	727
21	Maryland	717
22	Texas	699
23	Alabama	673
24	Iowa	654
25	Nevada	631
26	New Mexico	602
27	Missouri	578
28	Tennessee	578
29	Minnesota	531
30	Montana	512
31	Ohio	493
32	Wisconsin	493
33	North Carolina	465
34	California	464
35	Colorado	456
36	Idaho	449
37	Virginia	449
38	Kansas	434
39	Nebraska	427
40	Kentucky	399
41	Oklahoma	390
42	New Hampshire	369
43	Washington	352
44	West Virginia	333
45	Puerto Rico	297
46	Wyoming	267
47	Utah	228
48	Oregon	184
49	Hawaii	156
50	Alaska	128
51	Maine	123
52	Vermont	94

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	18
2	Arkansas	9
3	Montana	9
4	Iowa	8
5	New Mexico	8
6	South Dakota	8
7	Idaho	7
8	Rhode Island	7
9	Wisconsin	7
10	Illinois	6
11	Indiana	6
12	Nebraska	6
13	Wyoming	6
14	Tennessee	5
15	Colorado	4
16	Michigan	4
17	Minnesota	4
18	Mississippi	4
19	Missouri	4
20	Nevada	4
21	West Virginia	4
22	Alabama	3
23	Connecticut	3
24	Kentucky	3
25	Massachusetts	3
26	North Carolina	3
27	Puerto Rico	3
28	Florida	2
29	Louisiana	2
30	Maryland	2
31	New Jersey	2
32	Oklahoma	2
33	Pennsylvania	2
34	South Carolina	2
35	Texas	2
36	Utah	2
37	Washington	2
38	Delaware	1
39	Georgia	1
40	Kansas	1
41	New York	1
42	Ohio	1
43	Oregon	1
44	Virginia	1
45	Alaska	0
46	Arizona	0
47	California	0
48	District of Columbia	0
49	Hawaii	0
50	Maine	0
51	New Hampshire	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Norton	Kansas	185,600	1	99
Lincoln	Arkansas	181,588	2	99
Bon Homme	South Dakota	179,249	3	99
Chattahoochee	Georgia	177,409	4	99
Buffalo	South Dakota	170,234	5	99
Davidson	Tennessee	59,658	402	87
Richland	South Carolina	45,849	894	71
York	South Carolina	29,785	1894	39
Orange	California	21,468	2416	23
Pierce	Washington	16,522	2639	16

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,208	1	99
Jerauld	South Dakota	6,458	2	99
Hancock	Georgia	5,321	3	99
Emporia city	Virginia	5,238	4	99
Kenedy	Texas	4,950	5	99
Richland	South Carolina	693	1090	65
Davidson	Tennessee	562	1358	56
Orange	California	481	1555	50
York	South Carolina	434	1699	45
Pierce	Washington	322	2041	35

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.